MaskRCNN-Benchmark(Pytorch版本)训练自己的数据以及避坑指南

摘要：一安装地址版本首先要阅读官网说明的环境要求，千万不要一股脑直接安装，不然后面程序很有可能会报错一定要按上面的说明一步一步来，千万别省略，不然后面程序很有可能会报错二数据准备我要制作的原始数据格式是训练文件在一个

一、安装

地址：MaskRCNN-Benchmark(Pytorch版本)

首先要阅读官网说明的环境要求，千万不要一股脑直接安装，不然后面程序很有可能会报错！！！

PyTorch 1.0 from a nightly release. It will not work with 1.0 nor 1.0.1. Installation instructions can be found in https://pytorch.org/get-start...

torchvision from master
cocoapi
yacs
matplotlib
GCC >= 4.9
OpenCV

# first, make sure that your conda is setup properly with the right environment
# for that, check that `which conda`, `which pip` and `which python` points to the
# right path. From a clean conda env, this is what you need to do

conda create --name maskrcnn_benchmark
conda activate maskrcnn_benchmark

# this installs the right pip and dependencies for the fresh python
conda install ipython

# maskrcnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python

# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.0
conda install -c pytorch pytorch-nightly torchvision cudatoolkit=9.0

export INSTALL_DIR=$PWD

# install pycocotools
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

# install apex
cd $INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

# install PyTorch Detection
cd $INSTALL_DIR
git clone https://github.com/facebookresearch/maskrcnn-benchmark.git
cd maskrcnn-benchmark

# the following will install the lib with
# symbolic links, so that you can modify
# the files if you want and won"t need to
# re-build it
python setup.py build develop


unset INSTALL_DIR

# or if you are on macOS
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop

一定要按上面的说明一步一步来，千万别省略，不然后面程序很有可能会报错！！！

二、数据准备

我要制作的原始数据格式是训练文件在一个文件（train），标注文件是csv格式，内容如下：

第一步，先把全部有标记的图片且分为训练集，验证集，分别存储在两个文件夹中，代码如下：

#!/usr/bin/env python
# coding=UTF-8
"""
@Description: 
@Author: HuangQinJian
@LastEditors: HuangQinJian
@Date: 2019-05-01 12:56:08
@LastEditTime: 2019-05-01 13:11:38
"""
import pandas as pd
import random
import os
import shutil

if not os.path.exists("trained/"):
    os.mkdir("trained/")

if not os.path.exists("val/"):
    os.mkdir("val/")

val_rate = 0.15

img_path = "train/"
img_list = os.listdir(img_path)
train = pd.read_csv("train_label_fix.csv")
# print(img_list)
random.shuffle(img_list)

total_num = len(img_list)
val_num = int(total_num*val_rate)
train_num = total_num-val_num

for i in range(train_num):
    img_name = img_list[i]
    shutil.copy("train/" + img_name, "trained/" + img_name)
for j in range(val_num):
    img_name = img_list[j+train_num]
    shutil.copy("train/" + img_name, "val/" + img_name)

第二步，把csv格式的标注文件转换成coco的格式，代码如下：

#!/usr/bin/env python
# coding=UTF-8
"""
@Description: 
@Author: HuangQinJian
@LastEditors: HuangQinJian
@Date: 2019-04-23 11:28:23
@LastEditTime: 2019-05-01 13:15:57
"""
import sys
import os
import json
import cv2
import pandas as pd

START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = {}


def convert(csv_path, img_path, json_file):
    """
    csv_path : csv文件的路径
    img_path : 存放图片的文件夹
    json_file : 保存生成的json文件路径
    """
    json_dict = {"images": [], "type": "instances", "annotations": [],
                 "categories": []}
    bnd_id = START_BOUNDING_BOX_ID
    categories = PRE_DEFINE_CATEGORIES
    csv = pd.read_csv(csv_path)
    img_nameList = os.listdir(img_path)
    img_num = len(img_nameList)
    print("图片总数为{0}".format(img_num))
    for i in range(img_num):
        # for i in range(30):
        image_id = i+1
        img_name = img_nameList[i]
        if img_name == "60f3ea2534804c9b806e7d5ae1e229cf.jpg" or img_name == "6b292bacb2024d9b9f2d0620f489b1e4.jpg":
            continue
        # 可能需要根据具体格式修改的地方
        lines = csv[csv.filename == img_name]
        img = cv2.imread(os.path.join(img_path, img_name))
        height, width, _ = img.shape
        image = {"file_name": img_name, "height": height, "width": width,
                 "id": image_id}
        print(image)
        json_dict["images"].append(image)
        for j in range(len(lines)):
            # 可能需要根据具体格式修改的地方
            category = str(lines.iloc[j]["type"])
            if category not in categories:
                new_id = len(categories)
                categories[category] = new_id
            category_id = categories[category]
            # 可能需要根据具体格式修改的地方
            xmin = int(lines.iloc[j]["X1"])
            ymin = int(lines.iloc[j]["Y1"])
            xmax = int(lines.iloc[j]["X3"])
            ymax = int(lines.iloc[j]["Y3"])
            # print(xmin, ymin, xmax, ymax)
            assert(xmax > xmin)
            assert(ymax > ymin)
            o_width = abs(xmax - xmin)
            o_height = abs(ymax - ymin)
            ann = {"area": o_width*o_height, "iscrowd": 0, "image_id":
                   image_id, "bbox": [xmin, ymin, o_width, o_height],
                   "category_id": category_id, "id": bnd_id, "ignore": 0,
                   "segmentation": []}
            json_dict["annotations"].append(ann)
            bnd_id = bnd_id + 1
    for cate, cid in categories.items():
        cat = {"supercategory": "none", "id": cid, "name": cate}
        json_dict["categories"].append(cat)

    json_fp = open(json_file, "w")
    json_str = json.dumps(json_dict, indent=4)
    json_fp.write(json_str)
    json_fp.close()


if __name__ == "__main__":
    # csv_path = "data/train_label_fix.csv"
    # img_path = "data/train/"
    # json_file = "train.json"
    csv_path = "train_label_fix.csv"
    img_path = "trained/"
    json_file = "trained.json"
    convert(csv_path, img_path, json_file)
    csv_path = "train_label_fix.csv"
    img_path = "val/"
    json_file = "val.json"
    convert(csv_path, img_path, json_file)

第三步，可视化转换后的coco的格式，以确保转换正确，代码如下：

（注意：在这一步中，需要先下载 cocoapi , 可能出现的 问题）

#!/usr/bin/env python
# coding=UTF-8
"""
@Description: 
@Author: HuangQinJian
@LastEditors: HuangQinJian
@Date: 2019-04-23 13:43:24
@LastEditTime: 2019-04-30 21:29:26
"""
from pycocotools.coco import COCO
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
import cv2
import os
from skimage.io import imsave
import numpy as np
pylab.rcParams["figure.figsize"] = (8.0, 10.0)

img_path = "data/train/"
annFile = "train.json"

if not os.path.exists("anno_image_coco/"):
    os.makedirs("anno_image_coco/")


def draw_rectangle(coordinates, image, image_name):
    for coordinate in coordinates:
        left = np.rint(coordinate[0])
        right = np.rint(coordinate[1])
        top = np.rint(coordinate[2])
        bottom = np.rint(coordinate[3])
        # 左上角坐标, 右下角坐标
        cv2.rectangle(image,
                      (int(left), int(right)),
                      (int(top), int(bottom)),
                      (0, 255, 0),
                      2)
    imsave("anno_image_coco/"+image_name, image)


# 初始化标注数据的 COCO api
coco = COCO(annFile)

# display COCO categories and supercategories
cats = coco.loadCats(coco.getCatIds())
nms = [cat["name"] for cat in cats]
# print("COCO categories: 
{}
".format(" ".join(nms)))

nms = set([cat["supercategory"] for cat in cats])
# print("COCO supercategories: 
{}".format(" ".join(nms)))

img_path = "data/train/"
img_list = os.listdir(img_path)
# for i in range(len(img_list)):
for i in range(7):
    imgIds = i+1
    img = coco.loadImgs(imgIds)[0]
    image_name = img["file_name"]
    # print(img)

    # 加载并显示图片
    # I = io.imread("%s/%s" % (img_path, img["file_name"]))
    # plt.axis("off")
    # plt.imshow(I)
    # plt.show()

    # catIds=[] 说明展示所有类别的box，也可以指定类别
    annIds = coco.getAnnIds(imgIds=img["id"], catIds=[], iscrowd=None)
    anns = coco.loadAnns(annIds)
    # print(anns)
    coordinates = []
    img_raw = cv2.imread(os.path.join(img_path, image_name))
    for j in range(len(anns)):
        coordinate = []
        coordinate.append(anns[j]["bbox"][0])
        coordinate.append(anns[j]["bbox"][1]+anns[j]["bbox"][3])
        coordinate.append(anns[j]["bbox"][0]+anns[j]["bbox"][2])
        coordinate.append(anns[j]["bbox"][1])
        # print(coordinate)
        coordinates.append(coordinate)
    # print(coordinates)
    draw_rectangle(coordinates, img_raw, image_name)

三、文件配置

在训练自己的数据集过程中需要修改的地方可能很多，下面我就列出常用的几个：

修改maskrcnn_benchmark/config/paths_catalog.py中数据集路径：

class DatasetCatalog(object):
    # 看自己的实际情况修改路径！！！
    # 看自己的实际情况修改路径！！！
    # 看自己的实际情况修改路径！！！
    DATA_DIR = ""
    DATASETS = {
        "coco_2017_train": {
            "img_dir": "coco/train2017",
            "ann_file": "coco/annotations/instances_train2017.json"
        },
        "coco_2017_val": {
            "img_dir": "coco/val2017",
            "ann_file": "coco/annotations/instances_val2017.json"
        },
        # 改成训练集所在路径！！！
        # 改成训练集所在路径！！！
        # 改成训练集所在路径！！！
        "coco_2014_train": {
            "img_dir": "/data1/hqj/traffic-sign-identification/trained",
            "ann_file": "/data1/hqj/traffic-sign-identification/trained.json"
        },
        # 改成验证集所在路径！！！
        # 改成验证集所在路径！！！
        # 改成验证集所在路径！！！
        "coco_2014_val": {
            "img_dir": "/data1/hqj/traffic-sign-identification/val",
            "ann_file": "/data1/hqj/traffic-sign-identification/val.json"
        },
        # 改成测试集所在路径！！！
        # 改成测试集所在路径！！！
        # 改成测试集所在路径！！！
        "coco_2014_test": {
            "img_dir": "/data1/hqj/traffic-sign-identification/test"
        ...

config下的配置文件：

由于这个文件下的参数很多，往往需要根据自己的具体需求改，我就列出自己的配置（使用的是e2e_faster_rcnn_X_101_32x8d_FPN_1x.yaml，其中我有注释的必须改，比如 NUM_CLASSES）：

INPUT:
  MIN_SIZE_TRAIN: (1000,)
  MAX_SIZE_TRAIN: 1667
  MIN_SIZE_TEST: 1000
  MAX_SIZE_TEST: 1667
MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d"
  BACKBONE:
    CONV_BODY: "R-101-FPN"
  RPN:
    USE_FPN: True
    BATCH_SIZE_PER_IMAGE: 128
    ANCHOR_SIZES: (16, 32, 64, 128, 256)
    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
    PRE_NMS_TOP_N_TRAIN: 2000
    PRE_NMS_TOP_N_TEST: 1000
    POST_NMS_TOP_N_TEST: 1000
    FPN_POST_NMS_TOP_N_TEST: 1000
    FPN_POST_NMS_TOP_N_TRAIN: 1000
    ASPECT_RATIOS : (1.0,)
  FPN:
    USE_GN: True
  ROI_HEADS:
    # 是否使用FPN
    USE_FPN: True
  ROI_BOX_HEAD:
    USE_GN: True
    POOLER_RESOLUTION: 7
    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
    POOLER_SAMPLING_RATIO: 2
    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
    PREDICTOR: "FPNPredictor"
    # 修改成自己任务所需要检测的类别数+1
    NUM_CLASSES: 22
  RESNETS:
    BACKBONE_OUT_CHANNELS: 256
    STRIDE_IN_1X1: False
    NUM_GROUPS: 32
    WIDTH_PER_GROUP: 8
DATASETS:
  # paths_catalog.py文件中的配置，数据集指定时如果仅有一个数据集不要忘了逗号（如：("coco_2014_val",)）
  TRAIN: ("coco_2014_train",)
  TEST: ("coco_2014_val",)
DATALOADER:
  SIZE_DIVISIBILITY: 32
SOLVER:
  BASE_LR: 0.001
  WEIGHT_DECAY: 0.0001
  STEPS: (240000, 320000)
  MAX_ITER: 360000
  # 很重要的设置，具体可以参见官网说明：https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/README.md
  IMS_PER_BATCH: 1
  # 保存模型的间隔
  CHECKPOINT_PERIOD: 18000
# 输出文件路径
OUTPUT_DIR: "./weight/"

如果只做检测任务的话，删除 maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/coco.py 中 82-84这三行比较保险。

maskrcnn_benchmark/engine/trainer.py 中 第 90 行可设置输出日志的间隔（默认20，我感觉输出太频繁，看你自己）

四、模型训练

单GPU

官网给出的是：

python /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "/path/to/config/file.yaml"

但是这个默认会使用第一个GPU，如果想指定GPU的话，可以使用以下命令：

# 3是要使用GPU的ID
CUDA_VISIBLE_DEVICES=3 python /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "/path/to/config/file.yaml"

如果出现内存溢出的情况，这时候就需要调整参数，具体可以参见官网：内存溢出解决

多GPU

官网给出的是：

export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "path/to/config/file.yaml" MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN images_per_gpu x 1000

但是这个默认会随机使用GPU，如果想指定GPU的话，可以使用以下命令：

# --nproc_per_node=4 是指使用GPU的数目为4
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4  /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "path/to/config/file.yaml"

遗憾的是，多GPU在我的服务器上一直运行不成功，还请大家帮忙解决！！！

问题地址：Multi-GPU training error

五、模型验证

修改 config 配置文件中 WEIGHT: "../weight/model_final.pth"（此处应为训练完保存的权重）

运行命令：

CUDA_VISIBLE_DEVICES=5 python tools/test_net.py --config-file "/path/to/config/file.yaml" TEST.IMS_PER_BATCH 8

其中TEST.IMS_PER_BATCH 8也可以在config文件中直接配置：

TEST:
  IMS_PER_BATCH: 8

六、模型预测

修改 config 配置文件中 WEIGHT: "../weight/model_final.pth"（此处应为训练完保存的权重）

修改demo/predictor.py中 CATEGORIES ，替换成自己数据的物体类别（如果想可视化结果，没有可以不改，可以参考demo/下面的例子）：

class COCODemo(object):
    # COCO categories for pretty print
    CATEGORIES = [
        "__background",
        ...
    ]

新建一个文件 demo/predict.py（需要修改的地方已做注释）

#!/usr/bin/env python
# coding=UTF-8
"""
@Description:
@Author: HuangQinJian
@LastEditors: HuangQinJian
@Date: 2019-05-01 12:36:04
@LastEditTime: 2019-05-03 17:29:23
"""
import os

import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from PIL import Image

from maskrcnn_benchmark.config import cfg
from predictor import COCODemo
from tqdm import tqdm

# this makes our figures bigger
pylab.rcParams["figure.figsize"] = 20, 12

# 替换成自己的配置文件
# 替换成自己的配置文件
# 替换成自己的配置文件
config_file = "../configs/e2e_faster_rcnn_R_50_FPN_1x.yaml"

# update the config options with the config file
cfg.merge_from_file(config_file)
# manual override some options
cfg.merge_from_list(["MODEL.DEVICE", "cuda"])


def load(img_path):
    pil_image = Image.open(img_path).convert("RGB")
    # convert to BGR format
    image = np.array(pil_image)[:, :, [2, 1, 0]]
    return image

# 根据自己的需求改
# 根据自己的需求改
# 根据自己的需求改
coco_demo = COCODemo(
    cfg,
    min_image_size=1600,
    confidence_threshold=0.7,
)

# 测试图片的路径
# 测试图片的路径
# 测试图片的路径
imgs_dir = "/data1/hqj/traffic-sign-identification/test"
img_names = os.listdir(imgs_dir)

submit_v4 = pd.DataFrame()
empty_v4 = pd.DataFrame()

filenameList = []

X1List = []
X2List = []
X3List = []
X4List = []

Y1List = []
Y2List = []
Y3List = []
Y4List = []

TypeList = []

empty_img_name = []

# for img_name in img_names:
for i, img_name in enumerate(tqdm(img_names)):
    path = os.path.join(imgs_dir, img_name)
    image = load(path)
    # compute predictions
    predictions = coco_demo.compute_prediction(image)
    try:
        scores = predictions.get_field("scores").numpy()
        bbox = predictions.bbox[np.argmax(scores)].numpy()
        labelList = predictions.get_field("labels").numpy()
        label = labelList[np.argmax(scores)]

        filenameList.append(img_name)
        X1List.append(round(bbox[0]))
        Y1List.append(round(bbox[1]))
        X2List.append(round(bbox[2]))
        Y2List.append(round(bbox[1]))
        X3List.append(round(bbox[2]))
        Y3List.append(round(bbox[3]))
        X4List.append(round(bbox[0]))
        Y4List.append(round(bbox[3]))
        TypeList.append(label)
        # print(filenameList, X1List, X2List, X3List, X4List, Y1List,
        #       Y2List, Y3List, Y4List, TypeList)
        print(label)
    except:
        empty_img_name.append(img_name)
        print(empty_img_name)

submit_v4["filename"] = filenameList
submit_v4["X1"] = X1List
submit_v4["Y1"] = Y1List
submit_v4["X2"] = X2List
submit_v4["Y2"] = Y2List
submit_v4["X3"] = X3List
submit_v4["Y3"] = Y3List
submit_v4["X4"] = X4List
submit_v4["Y4"] = Y4List
submit_v4["type"] = TypeList

empty_v4["filename"] = empty_img_name

submit_v4.to_csv("submit_v4.csv", index=None)
empty_v4.to_csv("empty_v4.csv", index=None)

运行命令：

CUDA_VISIBLE_DEVICES=5  python demo/predict.py

七、结束语

1. 若有修改maskrcnn-benchmark文件夹下的代码，一定要重新编译！一定要重新编译！一定要重新编译！

2. 更多精彩内容，欢迎前往我的 CSDN